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Prediction Model of Ecological Environment Carrying Capacity in the Waters of Hainan Island.

Authors :
Ming Zhao
Shuaishuai Hao
Source :
Journal of Coastal Research. 3/9/2019, Vol. 93, p436-442. 7p. 1 Color Photograph, 1 Diagram, 2 Charts, 2 Graphs.
Publication Year :
2019

Abstract

In order to improve the optimal management and bearing control ability of Hainan boundary island waters ecological environment, it is necessary to optimize and predict the ecological environment carrying capacity of Hainan boundary island waters, and put forward the optimization prediction method of Hainan boundary island waters ecological environment carrying capacity based on association rule bearing control and fuzzy adaptive clustering. The statistical sequence distribution model of ecological environment carrying capacity of Hainan boundary island waters is constructed. Big data statistical information modeling of Hainan boundary island waters ecological environment carrying capacity is carried out by using big data mining method. The characteristics of association rules of Hainan boundary island waters ecological environment carrying capacity are extracted, and the automatic clustering treatment of Hainan boundary island waters ecological environment carrying capacity big data is carried out by using fuzzy clustering method. The optimal iterative model for predicting the ecological environment carrying capacity of Hainan boundary island waters is established, and the optimal prediction of ecological environment carrying capacity of Hainan boundary island waters is realized by combining the adaptive optimization algorithm. The simulation results show that the method has good adaptability and high prediction accuracy, and improves the adaptive bearing control and management ability of the ecological environment of Hainan boundary island waters. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07490208
Volume :
93
Database :
Academic Search Index
Journal :
Journal of Coastal Research
Publication Type :
Academic Journal
Accession number :
143091429
Full Text :
https://doi.org/10.2112/SI93-057.1